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不同微阵列平台之间存在高度协调性

对研究性微阵列的大规模交叉平台的研究发现不同平台之间存在高度协调性

验证了当前研究的主要依据的准确性

2006年7月2日

【DavidShi译文】【波士顿报道】基因表达微阵列已经在生物医学研究中产生了深刻影响。目前人们猜想该技术将扩大它当前的功能，成为基础科学研究的实验手段，并将日益应用于临床实践。在实现这一猜想之前，人们必须着重研究不同交叉平台间比较和数据的整合问题。现有各种平台和数据分析的多样性，已经让多种不同平台所产生数据之间的相互比较具有挑战性。不同实验室可能用不同的平台来测试相同的基因，那彼此间可比性如何呢？

温斯顿?帕特里克?郭，牙科博士，理学硕士，医学博士，哈佛牙科学院口腔和发育生物系博士后。他和他的同伴们试验了几乎所有现有商业性的和“内部”的基因表达微阵列平台，进行了交叉平台和交叉实验比较。据7月2日的《自然?生物技术》报道，他们为各平台和不同实验室间的比较提供了一个框架。

该研究紧跟多项大规模试验。这些试验试图建立微阵列实验的标准化操作规程（从探针注释到数据分析）。这些试验包括了微阵列实验的最少信息 (http://mged.org/Workgroups/MIAME/miame.html)、外源RNA控制联盟协定(http://www.cstl.nist.gov/biotech/Cell&TissueMeasurements/GeneExpression/ERCC.htm)和微阵列质量控制工程(http://www.fda.gov/nctr/science/centers/toxicoinformatics/maqc/)。此外，只有在实验充分可靠和给出足够信息的前提下，微阵列中如Gene Expression Omnibus和ArrayExpress等数据的存储和检索的主要入口才真正有用。有了多种平台的存在和相应的微阵列剩余数据，一个重要的问题是，检测基因的各平台间是否有差异，如果有，来自不同平台的数据如何实现比较和综合。

在过去的几年里，人们已经开展了多种小规模试验，并报告了相互矛盾的结果。在郭的研究中，基因表达手段包括10种不同的微阵列平台(Affymetrix, Applied Biosystems, Agilent Technologies, Compugen, GE Healthcare, Mergen, MWG BioTech, Operon, academic cDNA and an academic long oligonucleotide array)和2种不同的定量逆转录PCR方法(Applied Biosystems TaqMan Assays and Roche Diagnostics Universal Probe Library)。为交叉平台间的比较开发出一种有效的相互协调的框架结构是这项研究的一个关键目标。

郭的目标是：使用确定的分析技术和明确的性能评价方法，提供无偏差的结果；尽可能一致地为不同平台做实验；具有足够的通用性，以包含各种新开发出来的学术性的和商业性的平台。为了让不同平台的条件尽可能相似，他选择了2种独特的具有大的动态表达范围的RNA样品。

“我们证明了，通过数据的严格预处理，商业性微阵列比起“内部”阵列来有更好的一致性，无论是内部的一致性和和其他平台间的一致性，”郭说，“就大多数方法而言，单色平台的一致性高于双色平台。”在多数情况下，微阵列结果和定量逆转录PCR结果高度一致，对高表达基因而言更是如此，但在低表达基因中有差异。郭还发现，当同一平台产生的基因表达方法在不同实验室里，交叉实验间的差异显著小于交叉平台间的差异。

郭在2002年发表了一篇交叉平台论文，该文比较了当时2中最常见的平台－Affymetrix 和 cDNA arrays。结果不是很理想，数据分析受制于很多因素，其中一个问题是不同平台间基因的注释和对应映射。根据先前的经验，郭于是决定通过让渡引入本研究的优先权，从而获取各个平台探针序列，其中一些平台仍保留这些信息的所有权。

探针的序列与基因的一级结构（NCBI的参考序列－RefSeq和外显子参考序列－RefSeq exon）相配对。郭通过使用探针，以及和使用基于注释的(UniGene clusters and LocusLink identifiers) 配对比较之后发现，不同微阵列平台间方法的一致性得到提高。他还发现，用定量逆转录PCR确认微阵列结果时，进行仔细化验设计很重要。

“该研究的目标是阐明一个比较框架，该框架在序列水平和转录子相配对，”郭说，“所有探针的序列是已知的，并采用这样的分析方法，用于较大规模的率先试验，这是首次报道。结果显示，在目前的微阵列中，有很多不同的平台能提供优质的或者可靠的数据，对高表达基因而言，尤其如此；在这些平台之间有着良好的一致性。总体上来说，微阵列检测低表达基因的能力依然欠佳。然而，尽管基因表达分析的规范化有了相当的进展，在相当多的领域内，各个平台间并不一致。这样，诸多问题仍待研究。”

【下面为英文原文】

Large-scale cross-platform study of research microarrays uncovers high concordance across platforms

Verifies the veracity of a mainstay of contemporary research

EMBARGOED FOR RELEASE UNTIL: SUNDAY, JULY 2, 2006, 1pm US EST

BOSTON--Gene expression microarrays have made a profound impact on biomedical research. The current expectation is that this technology will extend its current role as an experimental tool for basic science research and be increasingly applied in clinical practice. Before this can occur, the issues of cross-platform comparison and integration of data must be addressed. The diversity of available platforms and analytical methods has made comparison of data from multiple platforms challenging. Different laboratories may use different platforms to profile the same genes, but how comparable are they?

Winston Patrick Kuo, DDS, MS, DMSc, post-doctoral researcher in the Department of Oral and Developmental Biology at the Harvard School of Dental Medicine, and colleagues tested nearly all the available commercial and "in-house" gene expression microarray platforms for cross-platform and cross-laboratory comparisons. They have produced a framework for comparisons across platforms and different laboratories, reported in the July 2 online edition of Nature Biotechnology.

This study comes on the heels of several large efforts to create standardized protocols for microarray experiments (from probe annotation to data analysis), such as the Minimum Information About a Microarray Experiment (http://mged.org/Workgroups/MIAME/miame.html), the External RNA Controls Consortium (http://www.cstl.nist.gov/biotech/Cell&TissueMeasurements/GeneExpression/ERCC.htm), and the Microarray Quality Control Project (http://www.fda.gov/nctr/science/centers/toxicoinformatics/maqc/). In addition, major portals for deposition and retrieval of microarray data, such as the Gene Expression Omnibus and ArrayExpress, will only be truly useful if experiments are sufficiently reliable and annotated so meaningful results can be extracted across different platforms. Given the existence of multiple platforms and the corresponding plethora of microarray data, an important issue is whether the platforms measure genes differently, and, if so, how data from different platforms can be compared or combined.

Over the past several years, several small-scale attempts have been made and the reported results have been conflicting. In Kuo's study, gene expression measurements were obtained from 10 different microarray platforms (Affymetrix, Applied Biosystems, Agilent Technologies, Compugen, GE Healthcare, Mergen, MWG BioTech, Operon, academic cDNA and an academic long oligonucleotide array) and two different QRT-PCR approaches (Applied Biosystems TaqMan Assays and Roche Diagnostics Universal Probe Library). It has been an emphasized goal during this effort to develop a sound and consistent framework for cross-platform comparisons.

Kuo aimed to provide unbiased results with clear metrics for evaluations of performance, using established analytical techniques, to conduct the experiments for different platforms as similarly as possible, and to be general enough to allow inclusion of novel academic and commercial platforms as they develop. In order to make the conditions for the different platforms as similar as possible, two distinct RNA samples with wide dynamic range of expression were selected.

"We demonstrated that, after stringent pre-processing of the data, commercial arrays were more consistent than 'in-house' arrays both on internal consistency and agreement with other platforms," Kuo said, "and by most measures, one-dye platforms were more consistent than two-dye platforms." For the most part, the microarray results were highly concordant with QRT-PCR results, especially for highly expressed genes, but variable for genes with lower expression values. Kuo also found that when gene expression measurements are generated from the same platform but conducted at different laboratories, cross-laboratory variation were significantly smaller than cross-platform variations.

In 2002, Kuo published a cross-platform paper comparing the two most common platforms at the time, Affymetrix and cDNA arrays. The results were not too optimistic, and the analyses were constrained by many factors--one issue was the annotation and mapping of the genes across platforms. So Kuo, based on prior experience, decided to obtain probe sequences from every platform as negotiated prior to the induction of a platform to the study, and for a few of the platforms, this information remains proprietary.

Using probe sequences that were matched at the sequence level (RefSeq and RefSeq exon), improved consistency of measurements across different microarray platforms was observed as compared to using annotation-based (UniGene clusters and LocusLink identifiers) matches. Kuo also found evidence for the importance of careful assay design when using QRT-PCR to confirm microarray results. Higher concordance with microarray data was observed where QRT-PCR assays targeted the same exon as the microarray probes.

"The goal of this study was to illuminate a comparison framework that matched the transcripts at the sequence level," said Kuo. "This is a first report with a relatively large-scale initiative in which the sequences of all probes were known and utilized in such an analysis. The results indicate in the current state of microarrays, there are many available platforms that provide good quality data, or reliability, especially on highly expressed genes, and that between these platforms, there is generally good agreement, or consistency. The ability to detect low expressed genes is still a limitation of microarrays in general. However, there are substantial areas where the platforms disagree, despite considerable developments towards standardization of gene expression profiling, and therefore many issues remain open for investigation."